Indirect feedback tuning apparatuses and methods for tuning photonic systems

ABSTRACT

Various indirect feedback tuning apparatuses and methods for tuning photonic systems are enabled. For instance, a system can perform operations, such as: determining a temperature of an optical device, determining, based on the temperature of the optical device and a feedback model, a tuning input to stabilize an optical signal, and performing, based on the tuning input, feedback tuning, wherein the feedback tuning comprises thermal tuning and electrical tuning.

CROSS REFERENCE TO RELATED APPLICATION

The subject patent application claims priority to U.S. ProvisionalPatent Appln. No. 62/974,661, filed Dec. 17, 2019, and entitled “AnIndirect Feedback Tuning Method for Photonic System,” the entirety ofwhich application is hereby incorporated by reference herein.

TECHNICAL FIELD

The present disclosure generally relates to tuning of photonic systemsand associated methods.

BACKGROUND

Photonic devices are revolutionizing computing systems by improvingenergy efficiency, bandwidth, and latency of data movement and dataprocessing. Photon-based systems possess tremendous advantages, ascompared to electron-based systems. For instance, data transmission bitrates of optical transceivers outpace electron-based systems.Additionally, optical neural networks process data more rapidly thantheir predecessors.

However, photonic systems also present obstacles, such as thermal andprocess variation. Photonic devices are known to be highly sensitive totemperature variation. This sensitivity is compounded by the fact thatphotonic devices are frequently placed in environments that experience alarge range of temperatures. Additionally, photonic devices inherentlypossess random process variations/deviations resulting frommanufacturing. This can lead to inconsistencies from device to device.As compared to traditional electronic circuits, variations in photoniccircuits can be more severe. The variations in photonic circuits can bemore severe for several reasons, such as: (i) high geometric and thermalsensitivities of optical parameters; (ii) more complex multi-particleinteractions for active photonic components, including photons, holes,and electrons; and (iii) serious process-induced parasitic effects inGiga-speed applications.

Thermal variation is typically caused by runtime heat variation andenvironmental temperature fluctuations. In silicon photonic networks,especially for multicore processors, the runtime heat variation isdominant since the core power can reach several watts and the core canhave a busy or idle status depending on the applications. When usingdynamic-voltage frequency-scaling (DVFS) techniques, the situation ofthermal variation becomes more complicated. Process variation can beinfluenced by the fabrication variability. Many geometrical parameters,such as the structure of the waveguide, the gap of the directionalcoupler, and the surface roughness and bending rate, can affect theperformance of optical components. The doping process is another sourceof process variation, particularly in active photonic components.

Thus, tuning of photonic devices is required. Tuning significantlyimproves bit error rates (BERs) of optical systems. Conventionalphotonic tuning, however, significantly increases overall powerconsumption and optical loss. Further, conventional photonic tuningsystems merely target temperature stabilization, and do not focus on theessential problem: reliability as reflected in the optical signalpassing through a device. Therefore, there exists a need for improvedphotonic tuning which reduces bit error rates and power consumption.

The above-described background relating to photonic systems is merelyintended to provide a contextual overview of some current issues and isnot intended to be exhaustive. Other contextual information may becomefurther apparent upon review of the following detailed description.

DESCRIPTION OF DRAWINGS

Various non-limiting embodiments of the subject disclosure are describedwith reference to the following figures, wherein like reference numeralsrefer to like parts throughout unless otherwise specified.

FIG. 1 illustrates a system in accordance with one or more embodimentsdescribed herein.

FIG. 2 illustrates a system in accordance with one or more embodimentsdescribed herein.

FIG. 3 illustrates a system in accordance with one or more embodimentsdescribed herein.

FIG. 4 illustrates a system in accordance with one or more embodimentsdescribed herein.

FIG. 5 illustrates a system in accordance with one or more embodimentsdescribed herein.

FIG. 6 illustrates a system in accordance with one or more embodimentsdescribed herein.

FIG. 7 illustrates an exemplary system logic in accordance with one ormore embodiments described herein.

FIG. 8 illustrates an exemplary system logic in accordance with one ormore embodiments described herein.

FIG. 9 is a chart of an original signal and reconstructed signals inaccordance with one or more embodiments described herein.

FIG. 10 is a block flow diagram for tuning a photonic device inaccordance with one or more embodiments described herein.

FIG. 11 is a block flow diagram for tuning a photonic device inaccordance with one or more embodiments described herein.

FIG. 12 is a block flow diagram for tuning a photonic device inaccordance with one or more embodiments described herein.

FIG. 13 illustrates a block diagram of an example, non-limitingoperating environment in which one or more embodiments described hereincan be facilitated.

DETAILED DESCRIPTION

Various specific details of the disclosed embodiments are provided inthe description below. One skilled in the art will recognize, however,that the techniques described herein can in some cases be practicedwithout one or more of the specific details, or with other methods,components, materials, etc. In other instances, well-known structures,materials, or operations are not shown or described in detail to avoidobscuring certain aspects.

Embodiments described herein provide apparatuses and methods that enableimproved photonic device and system reliability while reducing powerconsumption and physical footprint. Embodiments herein can significantlyimprove BERs (e.g., to 10⁻⁹ BER for reliable communications).Embodiments described herein can additionally supportwavelength-division multiplexing (WDM). With WDM, an optical bit rate inone waveguide can be further scaled up to 400 Gbps. Further embodimentscan be utilized in optical modulators, optical switches, opticalfilters, data centers, or other suitable applications. Embodimentsdescribed herein can require low amounts of energy for tuning and canutilize a low sampling rate.

Embodiments described herein can comprise a processing unit thatexecutes executable instructions to facilitate performance ofoperations, comprising: determining a temperature of an optical device,determining, based on the temperature of the optical device and afeedback model, a tuning input to stabilize an optical signal, andperforming, based on the tuning input, feedback tuning, wherein theindirect feedback tuning comprises thermal tuning and electrical tuning.

Performing thermal tuning can occur using a pulse-width modulationcircuit, a digital-to-analog conversion circuit, or a gain-tunableamplifier. Electrical tuning can occur using a digital-to-analogconversion or a gain-tunable amplifier. Electrical can comprise tuning avoltage or a current of the optical device.

Feedback models can be based on historical tuning information associatedwith optical devices described herein. Machine learning can be utilizedto repeatedly determine thermal effects and feedback models. Suchfeedback models can be associated with the optical device, surroundingenvironments, tuning systems/circuits themselves, historical tuninginformation associated with an optical device, an optical, thermal, orelectrical signal, or other suitable basis for models.

Temperatures of optical devices can be determined using one or moretemperature sensitive integrated circuits/circuit blocks.

A prediction of a device and a surrounding environment of the device anda can be determined. Predictions can be based on temperature, a powerlevel of a heater, or historical tuning information. No-thermal-tuningtemperature can be based on a feedback model, and a feedback model cancomprise a thermal model of an optical device. Such an optical devicecan comprise a microresonator or a Mach-Zehnder Interferometer. Themicroresonator can comprise a microring resonator, a microdiskresonator, or a different type of optical resonator. Systems and/orphotonic devices described herein can be communicatively coupled to aphotonic network, or computationally coupled to a photonic processingsystem, or coupled to a hybrid photonic and electronic system.

Additional embodiments described herein can comprise a method. Themethod can comprise determining, by a system operatively coupled to atleast one processing unit, feedback from group of photonic devices,determining, by the system and based on the feedback of the group ofphotonic devices, models associated with the group of photonic devices,and performing, by the system and based on the models, indirect feedbacktuning, wherein the indirect feedback tuning comprises thermal tuningand electrical tuning.

Photonic devices described herein can comprise a photonic switch,photonic modulator, a photonic filter, or other suitable photonicdevices. Determining feedback herein using indirect feedback tuning neednot utilize a port of the photonic device. Further, feedback can beperformed on a singularity or plurality of various photonic devices.

Methods described herein can comprise quantitively storing empiricalcurves or test curves of a predicted future feedback signal/tuning or acurrent feedback signal/tuning. Such a predicted future feedback signalcan be based on feedback and a model. Such models can comprise a modelof a tuning system, a model of a photonic device, a model of anenvironment surrounding a photonic device, or another suitable model.

Further embodiments described herein can comprise a machine-readablestorage medium, comprising executable instructions that, when executedby one or more processing units, facilitate performance of operations,comprising: determining indirect feedback tuning to perform on aphotonic device, comprising: indirectly determining an optical signal ofthe photonic device, determining, based on the optical signal of thephotonic device and a feedback model, the indirect feedback tuningrequired to decrease a bit error rate of the photonic device, andcausing a model-based controller to apply the indirect feedback tuning.

Indirect feedback tuning can comprise thermal tuning and electricaltuning. Indirect feedback can be determined using machine learning torepeatedly or dynamically determine thermal effects and the feedbackmodel, and wherein the feedback model is associated with the photonicdevice. It additionally be appreciated that such feedback models can bebased on optical models, thermal models, and/or electrical models.

The above aspects of the disclosure and/or other features of respectiveembodiments thereof are described in further detail with respect to therespective drawings below. It should be appreciated that additionalmanifestations, configurations, implementations, protocols, etc. can beutilized in connection with the following components described herein ordifferent/additional components as would be appreciated by one skilledin the art.

With reference to FIG. 1 , there is illustrated a system 100 forindirect feedback tuning in accordance with various embodimentsdescribed herein. The system 100 can comprise one or more of an opticaldevice 102, one or more of an indirect feedback sensor 104, one or moreof a controller 106, one or more of a thermal tuning driver 108, one ormore of a thermal tuning unit 110, one or more of an electrical tuningdriver 112, and one or more an electrical tuning unit 114.

The optical device 102 can comprise a microresonator or Mach-Zehnderinterferometers. Such microresonators can comprise microring resonators,microdisk resonators, or other suitable microresonators. It can beappreciated that while one optical device is depicted herein, theoptical device 102 can comprise a plurality of optical devices.

The indirect feedback sensor 104 can sense conditions, such as anoptical signal or a temperature of an optical device 102, anenvironmental temperature surrounding an optical device 102, a tuningcircuit associated with the optical device 102, or other suitableconditions. It can be appreciated that corresponding feedback can beused to tune optical devices or adjust their statuses. According to anembodiment, feedback does not comprise optical power intensities orphoto currents of photodetectors.

According to an embodiment, the indirect feedback sensor 104 cancomprise a 12-bit temperature sensor. The indirect feedback sensor 104can be based on a temperature-sensitive current source, and an amplifiedtemperature voltage can be further sampled by a successive approximationanalog to digital converter (SAR ADC). According to an embodiment, theindirect feedback sensor 104 can comprise the SAR ADC. Stated otherwise,an SAR ADC can be included in the indirect feedback sensor 104.According to an embodiment, the indirect feedback sensor can comprise atemperature-sensitive transistor(s).

The controller 106 can utilize models (as will be later discussed ingreater detail) to assist with optical tuning. Such models can beobtained, for instance, before tuning or established during tuning.According to an embodiment, such models can change over time, adaptingto changing conditions and associated outcomes. According to anembodiment, a controller 106 can utilize predictive control methods,optical control methods, or machine learning methods as will be laterdiscussed in greater detail, to generate models in accordance withvarious embodiments described herein. A model utilized by a controller106 can be based on a whole or a part of an optical device, a whole or apart of their respective tuning environment, or a whole or a part of anassociated tuning circuit.

Thermal tuning driver 108 can comprise a circuit to drive a thermaltuning unit 110. Thermal tuning driver 108 can utilize adigital-to-analog converter, a pulse-width-modulation circuit,digital-to-analog conversion circuit, or a gain-tunable amplifier. Thethermal tuning unit 110 can comprise a heater or a cooler. In thisregard, the thermal tuning unit 110 can adjust a temperature of anoptical device 102. It can be appreciated that thermal tuning unit 110can tune an optical device 102 by adjusting its temperature, acorresponding environmental temperature, and/or an associated tuningcircuit temperature.

Electrical tuning driver 112 can comprise a digital-to-analog converterand/or an amplifier (e.g., a gain-tunable amplifier). Electrical tuningdriver 112 can comprise circuits to drive an electrical tuning unit 114.An electrical tuning unit 114 can tune an optical device 102 by carriersand/or electrical fields. Associated methods to control carriers caninclude carrier injection, carrier depletion, and/or carrieraccumulation. It can be appreciated that the electrical tuning driver112 can tune a voltage or a current of an optical device 102.

With reference to FIG. 2 , there is illustrated a system 200 forindirect feedback tuning in accordance with various embodimentsdescribed herein. The system 200 can comprise one or more of an opticaldevice 102, one or more of an indirect feedback sensor 104, one or moreof a controller 106, one or more of a thermal tuning driver 108, one ormore of a thermal tuning unit 110, one or more of an electrical tuningdriver 112, and one or more of an electrical tuning unit 114. In thisregard, it can be appreciated that the system 200 can be similar to thesystem 100.

The system 200 can additionally comprise a network 202. In this regardthe optical device 102 can be communicatively coupled to the network202. According to an embodiment, the network 202 can comprise a photonicnetwork. According to another embodiment, the network 202 can comprise anetwork-on-chip. According to yet another embodiment, the network 202can comprise additional optical devices 102, connecting a plurality ofoptical device 102. In further embodiments, the network 202 can comprisea computationally coupled photonic processing system, or a coupledhybrid photonic and electronic system (e.g., a system comprisingphotonic and electronic systems or subsystems). In additionalembodiments, the network 202 can comprise an optical neutral network oran optical ranging system.

Turning now to FIG. 3 , there is illustrated an indirect feedback system300 and its associated inputs and outputs. The indirect feedback tuningsystem 300 can comprise one of more of each of the following: acontroller 302, comprising a processor or a processing unit 304, amemory 306, a thermal tuning driver component 308, an electrical tuningdriver component 310, a sensing component 312, and a machine learningcomponent 314.

According to an embodiment, the processing unit 304 can comprise aprocessor and a memory. According to another embodiment, the processingunit 304 and memory 306 are separate components/units.

The controller 302 can determine to use thermal tuning, or electricaltuning, or a combination of both thermal tuning and electrical tuning,based on models and/or feedback. Such a determination can be based ontuning speed, tuning accuracy, tuning range, tuning energy, tuningpower, tuning circuit cost, or other suitable factors. It can beappreciated that the controller 302 can be similar to the controller106.

The sensing component 312 can be utilized to receive an indirectfeedback 316. The sensing component 312 can, for instance, determine atemperature of a photonic device, a corresponding environmentaltemperature associated with a photonic device, or a tuning circuittemperature associated with a photonic device.

The electrical tuning driver component 310 can facilitate electricaltuning 318. The electrical tuning driver component 310 can comprise adigital-to-analog converter and/or an amplifier. The electrical tuningdriver component 310 can comprise circuits to facilitate electricaltuning, such as by carriers and/or electrical fields. Associated methodsto control carriers can include carrier injection, carrier depletion,and/or carrier accumulation. Electrical tuning can be fast and energyefficient.

The thermal tuning driver component 308 can facilitate thermal tuning320. Thermal tuning 320 can comprise a large tuning range. The thermaltuning driver component 308 can utilize a digital-to-analog converterand/or a pulse-width-modulation circuit. The thermal tuning drivercomponent 308 can comprise a heater or a cooler, or can becommunicatively coupled to a separate heater or a cooler. In thisregard, the thermal tuning driver component 308 can adjust a temperatureof an optical/photonic device via thermal tuning 320. It can beappreciated that thermal tuning can tune an optical/photonic device byadjusting its temperature, a corresponding environmental temperature, anassociated tuning circuit temperature, or another element or areasuitable for thermal tuning.

In various embodiments, a machine learning algorithm can be used (e.g.,by a machine learning component 314) to facilitate indirect feedbacktuning and/or associated photonic functions, such as thermal tuning,electrical tuning, photonic device modeling, MPC, or other suitablefunctions. In various cases, the machine learning algorithm can betrained (e.g., via supervised learning, unsupervised learning,reinforcement learning, and so on) to provide appropriate feedback toenable the lowest BER and/or lowest energy consumption, or to determinean appropriate balance between lowering BER and energy consumption(e.g., value tuning), or other suitable functions aided by machinelearning. The machine learning component 314 can repeatedly determinethermal effects and a feedback model associated with an optical device(e.g., an optical device 102).

In various embodiments, a trained machine learning and/or patternrecognition algorithm can include any suitable mathematical,statistical, and/or computational classification technique. Forinstance, in various embodiments, a trained machine learning and/orpattern recognition algorithm can include any suitable mathematical,statistical, and/or computational technique that can be trained (e.g.,via supervised learning on known data sets) to classify an input dataset into one or more output classifications (e.g., to detect patternsand/or signatures in an input data set and to correlate the detectedpatterns and/or signatures to one or more states of the input data set).In various embodiments, a trained machine learning and/or patternrecognition algorithm can comprise one or more linear classifiers (e.g.,generative classifiers such as Naïve Bayes, linear discriminantanalysis, and so on; discriminative classifiers such as logisticregression, perceptron, support vector machines, and so on; linearaffine transformations optimized to achieve global minima; and so on).In various embodiments, a trained machine learning and/or patternrecognition algorithm can comprise one or more non-linear classifiers(e.g., artificial neural networks, non-linear and/or high dimensionalsupport vector machines, and so on).

To facilitate the above-described machine learning aspects of variousembodiments of the subject claimed innovation, consider the followingdiscussion of artificial intelligence. Various embodiments of thepresent innovation herein can employ artificial intelligence (AI) tofacilitate automating one or more features of the present innovation.The components can employ various AI-based schemes for carrying outvarious embodiments/examples disclosed herein. In order to provide foror aid in the numerous determinations (e.g., determine, ascertain,infer, calculate, predict, prognose, estimate, derive, forecast, detect,compute, and so on) of the present innovation, components of the presentinnovation can examine the entirety or a subset of the data to which itis granted access and can provide for reasoning about or determinestates of the system, environment, and so on from a set of observationsas captured via events and/or data. Determinations can be employed toidentify a specific context or action, or can generate a probabilitydistribution over states, for example. The determinations can beprobabilistic; that is, the computation of a probability distributionover states of interest based on a consideration of data and events.Determinations can also refer to techniques employed for composinghigher-level events from a set of events and/or data.

Such determinations can result in the construction of new events oractions from a set of observed events and/or stored event data, whetheror not the events are correlated in close temporal proximity, andwhether the events and data come from one or several event and datasources. Components disclosed herein can employ various classification(explicitly trained (e.g., via training data) as well as implicitlytrained (e.g., via observing behavior, preferences, historicalinformation, receiving extrinsic information, and so on)) schemes and/orsystems (e.g., support vector machines, neural networks, expert systems,Bayesian belief networks, fuzzy logic, data fusion engines, and so on)in connection with performing automatic and/or determined action inconnection with the claimed subject matter. Thus, classification schemesand/or systems can be used to automatically learn and perform a numberof functions, actions, and/or determinations.

A classifier can map an input attribute vector, z=(z1, z2, z3, z4, zn),to a confidence that the input belongs to a class, as byf(z)=confidence(class). Such classification can employ a probabilisticand/or statistical-based analysis (e.g., factoring into the analysisutilities and costs) to determinate an action to be automaticallyperformed. A support vector machine (SVM) can be an example of aclassifier that can be employed. The SVM operates by finding ahyper-surface in the space of possible inputs, where the hyper-surfaceattempts to split the triggering criteria from the non-triggeringevents. Intuitively, this makes the classification correct for testingdata that is near, but not identical to training data. Other directedand undirected model classification approaches include, e.g., naïveBayes, Bayesian networks, decision trees, neural networks, fuzzy logicmodels, and/or probabilistic classification models providing differentpatterns of independence, any of which can be employed. Classificationas used herein also is inclusive of statistical regression that isutilized to develop models of priority.

Turning now to FIG. 4 , there is illustrated an indirect feedback system400 and its associated inputs and outputs. The indirect feedback tuningsystem 400 can comprise one of more of each of the following: acontroller 402, comprising a processor or a processing unit 304, amemory 306, a thermal tuning driver component 308, an electrical tuningdriver component 310, a sensing component 312, a machine learningcomponent 314, and a PID controller 404.

The PID controller 404 can utilize a proportioner (P term), anintegrator (I term), a differentiator (D term), and/or an adder. (e.g.,see FIG. 8 ). PID control function can be expressed as Equation (1)below. Hardware implementations of PID controllers comprise manyvariations. For instance, according to an embodiment, an integrator orthe differentiator can be omitted to save hardware overhead. PI and PDcontrollers comprise other derivations. A standard form of the PIDcontroller 404, as in Equation (1), can be discretized. The integralterm can be replaced as follows:K _(i)∫₀ ^(t) ^(k) e(τ)dτ=Σ ₀ ^(k)(t _(i))Δt.The derivative term can be replaced as (e(t_(k))−e(t_(k-1)))/Δt. Suchdiscretion can greatly reduce the logical synthesis area and can improvecomputation speed. The three operations can be analog, implemented bythe resistance, capacitance, and operation amplifier (e.g., see FIG. 8). When the input and output comprise analog signals, such an analog PIDcontroller 404 can avoid mixed signal issues and save the cost ofanalog-to-digital and digital-to-analog conversions.

$\begin{matrix}{{u(t)} = {{K_{p}{e(t)}} + {K_{i}{\int_{0}^{\tau}{{e(\tau)}d\tau}}} + {K_{d}\frac{{de}(t)}{dt}}}} & {{Equation}\mspace{14mu}(1)}\end{matrix}$

A PID controller 404 does not require direct knowledge of the tuningobjective, and its performance can depend on the parameters of K_(p),K_(i), and K_(d). An increase of K_(p) and K_(i) can add the settlingtime, causing overshoot and degrading the stability. A larger K_(p) cancause steady-state error, while K_(i) can eliminate the error. K_(d)does not affect such error and contributes to a decrease of overshoot.But, it worsen system stability when it is large. PID control targets alinear system in which, the system output of the linear combination ofinputs is equal to the same linear combination of outputs. However, athermal tuning system can comprise a classical nonlinear system becausethe passive cooling rate can be much slower than the active heatingrate. So, the overshoot temperature cannot be forced downward. Thecooling and heating rate can depend on current temperature. Furthermore,the initial process in a nonlinear system causes the oscillation of thesystem.

Model Predictive Control (MPC) can rely on dynamic models of the controlobjective, most typically the empirical model. It can be appreciatedthat a controller (e.g., a controller 402) can use or facilitate suchMPC. MPC can use this internal model to predict the future feedbacksignal and calculate optimal feedback or changes. A controllerleveraging MPC (e.g., a controller 402) can also record a to a memory306 history of past control moves to adjust the dynamic model. Suchhistory can comprise historical tuning information associated with anoptical device (e.g., an optical device 102). Hence, empirical knowledgeof the control objective can be important. According to an embodiment,empirical curves or test curves can be stored in a look-up tablequantitatively to save calculation time and logic synthesis area.According to an embodiment, controllers described herein (e.g., acontroller 402 or other controllers described herein) can comprise anMPC-type controller.

It can be appreciated that a controller 402 (or other controllersdescribed herein such as a controller 106, 302, or another controllerlater discussed) can facilitate indirect feedback tuning as disclosedherein. In indirect feedback tuning, process variation information of anoptical switch (e.g., an optical device 102) can be extracted in apackage test stage. This can help correct an ideal theoretical model, aswill be later discussed in greater detail. Environmental temperaturefluctuation (e.g., indirect feedback 316) can be monitored by atemperature sensor (e.g., sensing component 312). Temperatureinformation can be provided to an MPC-type controller (e.g., acontroller 402 or another suitable controller). According to anembodiment, the controller 402 can comprise a prediction model storedthereon (e.g., om a memory 306) and a PID controller 404. A processvariation model can predict a switch and no-thermal-tuning temperaturebased on current temperature and heater power. A process variation modelcan alternatively/additionally predict a device and/or an environmentsurrounding the device, based on temperature (of the device or theenvironment), a power level of a heater, or historical tuninginformation. This model can set the targeted temperature for the PIDcontroller 404 and the digital-to-analog conversion value for electricaltuning 318. Because tuning temperature can be linear to the heaterpower, it can directly reduce the complexity of the non-linear processof the receiving power. Prediction of no-thermal-tuning temperatureavoids overheating. Temperature prediction can be based on a thermalmodel of a microring switch instead as opposed to an overheatingthreshold.

Circuit designs as described herein can consider both performance andoverhead. The controller (e.g., a controller 106, 302, 402, or anothersuitable controller) can use the discrete implementation in Equation (2)below:u(t _(k))=u(t _(k-1))+K _(c)(et(t _(k))−et(t _(k-1)))+K _(i) et(t_(k))+K _(d)(et(t _(k))−2et(t _(k-1))+et(t _(k-2)))  Equation (2)

The temperature sensor (e.g., sensing component 312) can be based on atemperature sensitive integrated circuit, and the amplified temperaturevoltage can be further sampled by a successive approximationanalog-to-digital converter (SAR ADC) (e.g., an electrical tuning drivercomponent 310). Such a temperature sensor can utilize a temperaturedrift property of a microelectronic device, and can comprise a MetalOxide Silicon Field Effect Transistor or a Metal Oxide SemiconductorField Effect Transistor (MOSFET). In thermal tuning 320, a heater can bedriven by an associated PWM signal.

Turning now to FIG. 5 , there is illustrated an indirect feedback system500 and its associated inputs and outputs. The indirect feedback tuningsystem 500 can comprise one of more of each of the following: acontroller 502, comprising a processor or a processing unit 304, amemory 306, a thermal tuning driver component 308, an electrical tuningdriver component 310, a sensing component 312, a machine learningcomponent 314, and a PID controller 404.

Controller 502 can additionally comprise one or more of a thermal tuningunit 504. The thermal tuning unit 504 can comprise a heater or a cooler,which can drive thermal tuning 320. In this regard, the thermal tuningunit 504 can adjust a temperature of an optical device (e.g., an opticaldevice 102). It can be appreciated that thermal tuning unit 504 can tunean optical device (e.g., an optical device 102) by adjusting itstemperature, a corresponding environmental temperature, or an associatedtuning circuit temperature.

Controller 502 can additionally comprise one or more of an electricaltuning unit 506. An electrical tuning unit 506 can drive electricaltuning 318, and can tune an optical device (e.g., an optical device 102)by carriers and/or electrical fields. Associated methods to controlcarriers can include carrier injection, carrier depletion, and/orcarrier accumulation. It can be appreciated that the electrical tuningunit 506 can tune a voltage or a current of an optical device 102 viaelectrical tuning 318.

With reference to FIG. 6 , there is illustrated an indirect feedbacksystem 600 and its associated inputs and outputs. The indirect feedbacktuning system 500 can comprise one of more of each of the following: acontroller 502, comprising a processor or a processing unit 304, amemory 306, a thermal tuning driver component 308, an electrical tuningdriver component 310, a sensing component 312, a machine learningcomponent 314, a PID controller 404, thermal tuning unit 504, electricaltuning unit 506, and a communication unit 604.

According to an embodiment, the communication unit 604 can, forinstance, communicate to second or another controller. According to yetanother embodiment, the communication unit 604 can be utilized tocommunicatively couple the controller 602 to a silicon photonic network(e.g., a silicon photonic network 202). Communication unit 604 canleverage, for instance, wireless communication such as Wi-Fi, Bluetooth,telecommunication signals (e.g., 5G mmWave, sub-6, LTE, GSM, CDMA, orother suitable telecommunication protocols), or wired communication suchas fiber optic, copper, or other suitable wired connections. Thecommunication unit 604 can facilitate, for instance, failure reportingor status monitoring of a controller 602 or an associated opticaldevice.

With reference to FIG. 7 , there is illustrated a nonlimiting example ofa control logic 700 of a model-based controller (e.g., a controller 106,a controller 302, 402, 502, 602, or another suitable controller). Thecontrol logic 700 can comprise, at 702, indirectly monitoring a signal.In this regard, indirect signal monitoring can comprise portlessmonitoring. At 704, the signal can be compared to a model associatedwith the signal. The model can comprise known operations of opticaldevice across various conditions. Other embodiments can leverage machinelearning at 704 to generate accurate models in association with variousconditions. An observation taken by indirectly monitoring (e.g.,portless) a signal (e.g., with a sensing component 312 or an indirectfeedback sensor 104) can be compared to the model associated with thatsignal or a corresponding optical device. The model an additionallycomprise a model of an environment associated with a correspondingoptical device (e.g., a switch housing, network switch stack, a room inwhich the optical device is located, or another suitable environment tomodel), or tuning circuits associated with a corresponding opticaldevice. The model can account for thermal and/or process (e.g.,manufacturing) variations. The model can consider a sampling rate andsampling accuracy from a sensor, a tuning speed or tuning accuracy, toeffectively combine thermal and electrical tuning. Based on such acomparison, thermal tuning can be conducted at 710 if thermal tuning isdetermined to be necessary. Electrical tuning can be conducted at 708 ifelectrical tuning is determined to be necessary. If a failure or erroroccurs (e.g., in monitoring the signal or in a comparison to the model),an associated failure can be reported at 706. Failure can be reportedto, for example, an upper-level controller such as a network controller(e.g., on a network 202) or a system controller communicatively coupledto a network 202.

Turning now to FIG. 8 , there is illustrated a nonlimiting example of acontrol logic 800 of a model-based controller (e.g., a controller 106, acontroller 302, 402, 502, 602, or another suitable controller). PIDlogic 804 can receive a signal 810 which can be utilized to generate aPWM signal 806. Such a PWM signal 806 can drive a heater which can altera temperature of an optical device (e.g., an optical device 102). Amodel (as will be later discussed in greater detail) such as a model802, can be utilized in the PID logic 804 for temperature control. Model802 can also be utilized in digital to analog conversion 808. In thisregard, it can be appreciated that circuit implementation of controllogic 800 can comprise a temperature sensor (e.g., indirect feedbacksensor 104), MPC controller (e.g., controller 106), PWM 806 for thermaltuning and DAC 808 for electrical tuning.

Next, sampling and tuning in accordance with various embodimentsdescribed herein are further discussed. A sampling rate can be given byan input signal. For optical power feedback tuning, the optical power ofthe data signal can be sampled by an optical receiver in every bit sothat the sampling rate can be determined by the bit rate. BER feedbacktuning can slower, and its rate can be decided by a BER requirement.According to the Nyquist sampling theorem, the waveform should besampled over twice as fast as its highest frequency component.Furthermore, the highest frequency component can be determined by theprecision and sensing range of the temperature sensor. When thetemperature sensor (e.g., indirect feedback sensor 104) operates fromabout 0° C. to about 100° C., resolution of the 12-bit temperaturesensor can be around 0.025° C. The highest frequency component detectedby such a 12-bit temperature sensor can be around 1000 Hz (see, e.g.,FIG. 9 ). Most of effective frequency components (whose magnitude islarger than 0.025° C.) are lower than 1000 Hz. So, according to anembodiment, the Nyquist sampling rate is no more than 2000 Hz.

Next, various models in accordance with various embodiments describedherein will be discussed and enabled:

First, a process variation model is enabled. Process variation occursfrom inherent variations resulting from the fabrication process stepsand can affects many device parameters. For example, thermal oxidationor deposition may introduce cladding thickness variation. Masking,exposition, and etching can affect planar geometry size. Specifically,the line width of metals, polymers, and waveguides, the gap of thegrating coupler or directional coupler, and the curve of the ring canall be influential. Moreover, for exposition, using a positivephotoresist or negative photoresist also contributes to the processvariation. The etching process can affect the surface roughness of thewaveguide, and when using plasma etching, the angle of the plasmaetching process determines the tilt rate of the optical waveguide. Thedoping and annealing process can cause variations in carrier density anddistribution. The properties of optical components are more sensitive totheir geometry, compared to electrical components.

The process variations can be further separated into inter-die variationand intra-die variation. Intra-die variation is highly related to theX-Y position of each device in a chipset. Width and film thicknessvariation models can be evaluated. Because width variation and gapvariation can be caused by the masking, exposition, and etching, butwith different effects due to the photoresist, it can be assumed thatthe gap variation map can be inversely proportional to the widthvariation. The passing loss can be affected by the surface roughness andmaterial absorption.

After obtaining the material-level parameter variations, the photoniccomponent features can be extracted with Monte Carlo simulation using adevice simulator (e.g., BOSIM). It can be appreciated that a feedbackmodel can be associated with a photonic device, which can be extractedfrom an accurate simulator. Many stochastic properties at a circuitlevel might not be consistent with those at the material level.Significantly, these circuit level parameters can disobey the commonlyassumed Gaussian distributions. Also, circuit level parameters candepend on the X-Y locations.

Next, a thermal variation model is enabled. Thermal variation can becaused by runtime heat consumption and environmental temperaturefluctuation. Compared to ˜K scale environmental temperature fluctuationin one day, the runtime heat consumption, typically at ˜10K scale withina few minutes, can dominate the thermal variation analyses. In amulticore system using a silicon photonic network, the processing corecan contribute the most heat. The runtime thermal map can vary indifferent benchmarks, architectures, and clock cycles. First,computation-intensive benchmarks distribute tasks equally into most ofthe processing cores. Therefore, after initialization, more cores can“wake up” to heat the chip. Global temperatures can continue to risebefore a computing process is finished. Second, local temperatures ofthe chips may not be even because workload is not always mapped into allthe cores, and all the active cores may not finish tasks at the sametime.

Next, a device model is enabled. The thermal heater can be a keycomponent in thermal tuning. Power-related heater efficiency andtime-related heater delay can be two important evaluation metrics withrespect to a device model. Classified by material, there are metalheaters, polysilicon heaters, silicon heaters, or other suitableheaters. Classified by position, there are top heaters (e.g., waveguideheaters), substrate heaters, embedded heaters, or other suitableheaters. The differences in materials and positions affect the thermaldiffusion, the thermal resistance, the thermal capacitance, and thedevice temperature map. Equation (3) below shows a thermal model basedon a differential heat equation:

$\begin{matrix}{\frac{\partial u}{\partial t} = {{\alpha{\nabla^{2}u}} + f}} & {{Equation}\mspace{14mu}(3)} \\{{u = {u_{D}\mspace{14mu}{in}\mspace{14mu}\Omega}},{u = {{u_{0}\mspace{14mu}{at}\mspace{14mu} t} = 0}}} & \;\end{matrix}$α is the thermal diffusivity, and f is the heat source. Morespecifically,

$\alpha = {\frac{k}{c_{p}}\rho}$and k is thermal conductivity. c_(p) is the heat capacity, and ρ is thedensity. α is different in each layer. u_(D) is the boundary condition,and u₀ is the initial situation. Equation (4) provides the circuit levelvariant of Equation (3).

$\begin{matrix}{{{C_{th}\frac{{dT}(t)}{dt}} + \frac{T_{1} - T_{2}}{R_{th}}} = Q} & {{Equation}\mspace{14mu}(4)}\end{matrix}$C_(th)=ρc_(p)V is the thermal capacitance or thermal mass, V is thevolume of the material, T is the temperature and R_(th) is the thermalcapacitance.

Next, feedback in accordance with embodiments herein is discussed.Feedback signals can be separated into two categories based on theirsource: direct feedback signals and indirect feedback signals. Since itis desired for optical signals to be stable, avoiding variations, thedirect feedback signal can be the receiving power. The vulnerablereceiving power affects the network quality so that the networkevaluation metrics can be indirect feedback signals, such as the BER and1/0 bit statistics. Moreover, unstable optical power can be caused bythe temperature drift and/or process shift of photonic devices. Theprocess shift can be determined as soon as the fabrication is completed.The runtime temperature variations can be detected by the temperaturesensor. Together with an accurate model, the receiving optical power canbe obtained from the temperature data. Thus, the temperature can be anindirect feedback signal as well.

A temperature sensor can monitor the local environment temperature onthe chip. If the relationship between temperature and optical power isavailable, optical power can be determined with the detectedtemperature. Due to the process variation, the P_(drop)/P_(in) curvesmay be shifted or deformed.

The adoption of temperature feedback has several advantages. First, thetemperature fluctuation can be the only source of thermal variation.Temperature feedback can be directly related to a stable thermalenvironment. Second, in the limited temperature precision, thetemperature change can be significantly slower than the speed of anoptical signal. So, the sampling frequency of the feedback signal can belower, contributing to a lower hardware cost. Third, compared to afeedback optical signal, this indirect feedback signal does not occupythe port of the switch so it can be applied in more complex networktopologies. Fourth, the temperature sensor saves more energy and areathan the receiver circuit involving a transimpedance amplifier (TIA).

Next, tuning methods in accordance with embodiments herein arediscussed. The adjustments to compensate for variations can target manycomponents in the link. In the source, laser power and channelwavelength can be modified to compensate for the signal attenuationvariation and wavelength shift. In the destination, gain of the TIA canbe controlled. On the switch side, the optical switch can be tuned by athermal heater or bias voltage. The following are implementations andfeatures of different tuning methods:

(1) Electrical tuning (e.g., electrical tuning 318, electrical tuning708, as facilitated by electrical tuning driver 112, electrical tuningunit 114, electrical tuning driver component 310, or electrical tuningunit 506): When the bias voltage Vic of the optical switch increases andmore carriers are injected into the phase shifter, the transmissionspectrum has a blue-shift (e.g., a left shift or decrease in wavelength)with the shape deformation. The biasing circuit can be implemented inmany ways. For instance, the biasing can be added into a DAC(digital-to-analog converter) as a digital offset. The offset value canbe determined after the control logic. Analog biasing solutions are alsoavailable, such by feeding back more DC current and adjusting the DCoperating point in the amplifier-based biasing circuit.

Another type of electrical tuning is to tune the peak-peak voltageV_(pp) of the dynamic signal. It can be appreciated that, a largerV_(pp) causes a larger wavelength shift from V_(min) to V_(max), so thata temperature channel width of an on-state increases. This can increasetemperature tolerance. The analog adjustment of V_(pp) often involves anamplifier by changing its gain of an AC signal. Through thedigital-to-analog conversion, the number difference of V_(min) andV_(max) can be set.

Electrical tuning is fast and energy-efficient, compared to thermaltuning. Its tuning range, however, is limited. For instance, electricaltuning can only compensate for limited temperature of less than 5° C.

(2) Thermal tuning (e.g., thermal tuning 320, thermal tuning 710, asfacilitated by thermal tuning driver 108, thermal tuning unit 110,thermal tuning driver component 308, or thermal tuning unit 504): Thethermal heater can dominate the tuning of temperature. For the coolingprocess, the heater sink and active cooling system cannot dissipate heatlocally in ˜100 μm² scale. So, the heater sink and active cooling systemare more often used to reduce global temperature and increase the globalcooling rate. For the heating process, an on-chip thermal heater can beadopted. When the electric current passes a conductor or semiconductor,it produces Joule heating or resistive heating. This kind of heat can beutilized for thermal tuning. Targeting at control, the heater efficiencyand delay are a concern. The driving circuit of the heater can besimpler as compared to DAC or amplifier-involved electrical tuning.Since the temperature change can be much slower than the system clockfrequency, and the heat process is not sensitive to high-frequencyelectrical noise, the PWM signal can be adopted, which can be producedby a digital timer. The delay can be considerable, around μs/° C.depending on the temperature. Thermal tuning can achieve a high tuningrange but have low responsivity and cost more energy, compared toelectrical tuning.

FIG. 10 is a flow diagram of a process 1000 for indirect feedback tuningin accordance with one or more embodiments described herein. At 1002, atemperature of an optical device is determined. At 1004, a tuning inputto stabilize an optical signal is determined based on the temperature ofthe optical device and a feedback model. At 1006, feedback tuning isperformed, based on the tuning input, wherein the feedback tuningcomprises thermal tuning and electrical tuning.

FIG. 11 is a flow diagram of a process 1100 for indirect feedback tuningin accordance with one or more embodiments described herein. At 1102,feedback from a group of photonic devices is determined by a systemoperatively coupled to at least one processing unit. At 1104, modelsassociated with the group of photonic devices determined, based on thefeedback of the group of photonic devices. At 1106, indirect feedbacktuning is performed by the system and based on the models, wherein theindirect feedback tuning comprises thermal tuning and electrical tuning.

FIG. 12 is a flow diagram of a process 1200 for indirect feedback tuningin accordance with one or more embodiments described herein. At 1202,indirect feedback tuning to perform on a photonic device is determined.At 1204, an optical signal (or a temperature) of the photonic switch isindirectly determined. At 1206, indirect feedback tuning required todecrease a bit error rate of the photonic device are determined, basedon the optical signal (or the temperature) of the optical device and afeedback model. At 1208, a model-based controller is caused to apply theindirect feedback tuning.

FIGS. 10-12 illustrate respective methods or systems in accordance withcertain aspects of this disclosure. While, for purposes of simplicity ofexplanation, the methods or systems are shown and described as a seriesof acts, it is to be understood and appreciated that this disclosure isnot limited by the order of acts, as some acts may occur in differentorders and/or concurrently with other acts from those shown anddescribed herein. For example, those skilled in the art will understandand appreciate that methods can alternatively be represented as a seriesof interrelated states or events, such as in a state diagram. Moreover,not all illustrated acts may be required to implement methods inaccordance with certain aspects of this disclosure.

One or more embodiments can be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product can include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor or a processing unit to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium can be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention can be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions can executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer can be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection can be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) can execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It can be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions can be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionscan also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions can also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams can represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks can occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks cansometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

In connection with FIG. 13 , the systems and processes described belowcan be embodied within hardware, such as a single integrated circuit(IC) chip, multiple ICs, an application specific integrated circuit(ASIC), a customized circuit block, or the like. Further, the order inwhich some or all of the process blocks appear in each process shouldnot be deemed limiting. Rather, it should be understood that some of theprocess blocks can be executed in a variety of orders, not all of whichcan be explicitly illustrated herein.

With reference to FIG. 13 , an example environment 1300 for implementingvarious aspects of the claimed subject matter includes a computer 1302.The computer 1302 includes a processing unit 1304, a system memory 1306,a codec 1335, and a system bus 1308. The system bus 1308 couples systemcomponents including, but not limited to, the system memory 1306 to theprocessing unit 1304. The processing unit 1304 can be any of variousavailable processors. Dual microprocessors and other multiprocessorarchitectures also can be employed as the processing unit 1304.

The system bus 1308 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, or a local bus using any variety of available busarchitectures including, but not limited to, Industrial StandardArchitecture (ISA), Micro-Channel Architecture (MSA), Extended ISA(EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus(USB), Advanced Graphics Port (AGP), Personal Computer Memory CardInternational Association bus (PCMCIA), Firewire (IEEE 1394), and SmallComputer Systems Interface (SCSI).

The system memory 1306 includes volatile memory 1310 and non-volatilememory 1312, which can employ one or more of the disclosed memoryarchitectures, in various embodiments. The basic input/output system(BIOS), containing the basic routines to transfer information betweenelements within the computer 1302, such as during start-up, is stored innon-volatile memory 1312. In addition, according to present innovations,codec 1335 can include at least one of an encoder or decoder, whereinthe at least one of an encoder or decoder can consist of hardware,software, or a combination of hardware and software. Although, codec1335 is depicted as a separate component, codec 1335 can be containedwithin non-volatile memory 1312. By way of illustration, and notlimitation, non-volatile memory 1312 can include read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable programmable ROM (EEPROM), Flash memory, 3D Flashmemory, or resistive memory such as resistive random access memory(RRAM). Non-volatile memory 1312 can employ one or more of the disclosedmemory devices, in at least some embodiments. Moreover, non-volatilememory 1312 can be computer memory (e.g., physically integrated withcomputer 1302 or a mainboard thereof), or removable memory. Examples ofsuitable removable memory with which disclosed embodiments can beimplemented can include a secure digital (SD) card, a compact Flash (CF)card, a universal serial bus (USB) memory stick, or the like. Volatilememory 1310 includes random access memory (RAM), which acts as externalcache memory, and can also employ one or more disclosed memory devicesin various embodiments. By way of illustration and not limitation, RAMis available in many forms such as static RAM (SRAM), dynamic RAM(DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM),and enhanced SDRAM (ESDRAM) and so forth.

Computer 1302 can also include removable/non-removable,volatile/non-volatile computer storage medium. FIG. 13 illustrates, forexample, disk storage 1314. Disk storage 1314 includes, but is notlimited to, devices like a magnetic disk drive, solid state disk (SSD),flash memory card, or memory stick. In addition, disk storage 1314 caninclude storage medium separately or in combination with other storagemedium including, but not limited to, an optical disk drive such as acompact disk ROM device (CD-ROM), CD recordable drive (CD-R Drive), CDrewritable drive (CD-RW Drive) or a digital versatile disk ROM drive(DVD-ROM). To facilitate connection of the disk storage 1314 to thesystem bus 1308, a removable or non-removable interface is typicallyused, such as interface 1316. It is appreciated that disk storage 1314can store information related to a user. Such information might bestored at or provided to a server or to an application running on a userdevice. In one embodiment, the user can be notified (e.g., by way ofoutput device(s) 1336) of the types of information that are stored todisk storage 1314 or transmitted to the server or application. The usercan be provided the opportunity to opt-in or opt-out of having suchinformation collected or shared with the server or application (e.g., byway of input from input device(s) 1328).

It is to be appreciated that FIG. 13 describes software that acts as anintermediary between users and the basic computer resources described inthe suitable operating environment 1300. Such software includes anoperating system 1318. Operating system 1318, which can be stored ondisk storage 1314, acts to control and allocate resources of thecomputer 1302. Applications 1320 take advantage of the management ofresources by operating system 1318 through program modules 1324, andprogram data 1326, such as the boot/shutdown transaction table and thelike, stored either in system memory 1306 or on disk storage 1314. It isto be appreciated that the claimed subject matter can be implementedwith various operating systems or combinations of operating systems.

A user enters commands or information into the computer 1302 throughinput device(s) 1328. Input devices 1328 include, but are not limitedto, a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices connect to the processing unit 1304through the system bus 1308 via interface port(s) 1330. Interfaceport(s) 1330 include, for example, a serial port, a parallel port, agame port, and a universal serial bus (USB). Output device(s) 1336 usesome of the same type of ports as input device(s) 1328. Thus, forexample, a USB port can be used to provide input to computer 1302 and tooutput information from computer 1302 to an output device 1336. Outputadapter 1334 is provided to illustrate that there are some outputdevices 1336 like monitors, speakers, and printers, among other outputdevices 1336, which require special adapters. The output adapters 1334include, by way of illustration and not limitation, video and soundcards that provide a means of connection between the output device 1336and the system bus 1308. It should be noted that other devices orsystems of devices provide both input and output capabilities such asremote computer(s) 1338.

Computer 1302 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1338. The remote computer(s) 1338 can be a personal computer, a server,a router, a network PC, a workstation, a microprocessor based appliance,a peer device, a smart phone, a tablet, or other network node, andtypically includes many of the elements described relative to computer1302. For purposes of brevity, only a memory storage device 1340 isillustrated with remote computer(s) 1338. Remote computer(s) 1338 islogically connected to computer 1302 through a network interface 1342and then connected via communication connection(s) 1344. Networkinterface 1342 encompasses wire or wireless communication networks suchas local-area networks (LAN) and wide-area networks (WAN) and cellularnetworks. LAN technologies include Fiber Distributed Data Interface(FDDI), Copper Distributed Data Interface (CDDI), Ethernet, Token Ringand the like. WAN technologies include, but are not limited to,point-to-point links, circuit switching networks like IntegratedServices Digital Networks (ISDN) and variations thereon, packetswitching networks, and Digital Subscriber Lines (DSL).

Communication connection(s) 1344 refers to the hardware/softwareemployed to connect the network interface 1342 to the bus 1308. Whilecommunication connection 1344 is shown for illustrative clarity insidecomputer 1302, it can also be external to computer 1302. Thehardware/software necessary for connection to the network interface 1342includes, for exemplary purposes only, internal and externaltechnologies such as, modems including regular telephone grade modems,cable modems and DSL modems, ISDN adapters, and wired and wirelessEthernet cards, hubs, and routers.

While the subject matter has been described above in the general contextof computer-executable instructions of a computer program product thatruns on a computer and/or computers, those skilled in the art willrecognize that this disclosure also can or can be implemented incombination with other program modules. Generally, program modulesinclude routines, programs, components, data structures, etc. thatperform particular tasks and/or implement particular abstract datatypes. Moreover, those skilled in the art will appreciate that theinventive computer-implemented methods can be practiced with othercomputer system configurations, including single-processor ormultiprocessor computer systems, mini-computing devices, mainframecomputers, as well as computers, hand-held computing devices (e.g., PDA,phone), microprocessor-based or programmable consumer or industrialelectronics, and the like. The illustrated aspects can also be practicedin distributed computing environments where tasks are performed byremote processing devices that are linked through a communicationsnetwork. However, some, if not all aspects of this disclosure can bepracticed on stand-alone computers. In a distributed computingenvironment, program modules can be located in both local and remotememory storage devices.

As used in this application, the terms “component,” “system,”“platform,” “interface,” and the like, can refer to and/or can include acomputer-related entity or an entity related to an operational machinewith one or more specific functionalities. The entities disclosed hereincan be either hardware, a combination of hardware and software,software, or software in execution. For example, a component can be, butis not limited to being, a process running on a processor or aprocessing unit, a processor, a processing unit, an object, anexecutable, a thread of execution, a program, and/or a computer. By wayof illustration, both an application running on a server and the servercan be a component. One or more components can reside within a processand/or thread of execution and a component can be localized on onecomputer and/or distributed between two or more computers. In anotherexample, respective components can execute from various computerreadable media having various data structures stored thereon. Thecomponents can communicate via local and/or remote processes such as inaccordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry, which is operated by asoftware or firmware application executed by a processor. In such acase, the processor can be internal or external to the apparatus and canexecute at least a part of the software or firmware application. As yetanother example, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,wherein the electronic components can include a processor or other meansto execute software or firmware that confers at least in part thefunctionality of the electronic components. In an aspect, a componentcan emulate an electronic component via a virtual machine, e.g., withina cloud computing system.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form. As used herein, the terms “example”and/or “exemplary” are utilized to mean serving as an example, instance,or illustration and are intended to be non-limiting. For the avoidanceof doubt, the subject matter disclosed herein is not limited by suchexamples. In addition, any aspect or design described herein as an“example” and/or “exemplary” is not necessarily to be construed aspreferred or advantageous over other aspects or designs, nor is it meantto preclude equivalent exemplary structures and techniques known tothose of ordinary skill in the art.

As it is employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Further, processors can exploit nano-scalearchitectures such as, but not limited to, molecular and quantum-dotbased transistors, switches and gates, in order to optimize space usageor enhance performance of user equipment. A processor can also beimplemented as a combination of computing processing units. In thisdisclosure, terms such as “store,” “storage,” “data store,” datastorage,” “database,” and substantially any other information storagecomponent relevant to operation and functionality of a component areutilized to refer to “memory components,” entities embodied in a“memory,” or components comprising a memory. It is to be appreciatedthat memory and/or memory components described herein can be eithervolatile memory or nonvolatile memory, or can include both volatile andnonvolatile memory. By way of illustration, and not limitation,nonvolatile memory can include read only memory (ROM), programmable ROM(PROM), electrically programmable ROM (EPROM), electrically erasable ROM(EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g.,ferroelectric RAM (FeRAM). Volatile memory can include RAM, which canact as external cache memory, for example. By way of illustration andnot limitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM),direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), andRambus dynamic RAM (RDRAM). Additionally, the disclosed memorycomponents of systems or computer-implemented methods herein areintended to include, without being limited to including, these and anyother suitable types of memory.

The above description includes non-limiting examples of the variousembodiments. It is, of course, not possible to describe everyconceivable combination of components or methods for purposes ofdescribing the disclosed subject matter, and one skilled in the art mayrecognize that further combinations and permutations of the variousembodiments are possible. The disclosed subject matter is intended toembrace all such alterations, modifications, and variations that fallwithin the spirit and scope of the appended claims.

With regard to the various functions performed by the above describedcomponents, devices, circuits, systems, etc., the terms (including areference to a “means”) used to describe such components are intended toalso include, unless otherwise indicated, any structure(s) whichperforms the specified function of the described component (e.g., afunctional equivalent), even if not structurally equivalent to thedisclosed structure. In addition, while a particular feature of thedisclosed subject matter may have been disclosed with respect to onlyone of several implementations, such feature may be combined with one ormore other features of the other implementations as may be desired andadvantageous for any given or particular application.

The terms “exemplary” and/or “demonstrative” as used herein are intendedto mean serving as an example, instance, or illustration. For theavoidance of doubt, the subject matter disclosed herein is not limitedby such examples. In addition, any aspect or design described herein as“exemplary” and/or “demonstrative” is not necessarily to be construed aspreferred or advantageous over other aspects or designs, nor is it meantto preclude equivalent structures and techniques known to one skilled inthe art. Furthermore, to the extent that the terms “includes,” “has,”“contains,” and other similar words are used in either the detaileddescription or the claims, such terms are intended to be inclusive—in amanner similar to the term “comprising” as an open transitionword—without precluding any additional or other elements.

The term “or” as used herein is intended to mean an inclusive “or”rather than an exclusive “or.” For example, the phrase “A or B” isintended to include instances of A, B, and both A and B. Additionally,the articles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unless eitherotherwise specified or clear from the context to be directed to asingular form.

The term “set” as employed herein excludes the empty set, i.e., the setwith no elements therein. Thus, a “set” in the subject disclosureincludes one or more elements or entities. Likewise, the term “group” asutilized herein refers to a collection of one or more entities.

The description of illustrated embodiments of the subject disclosure asprovided herein, including what is described in the Abstract, is notintended to be exhaustive or to limit the disclosed embodiments to theprecise forms disclosed. While specific embodiments and examples aredescribed herein for illustrative purposes, various modifications arepossible that are considered within the scope of such embodiments andexamples, as one skilled in the art can recognize. In this regard, whilethe subject matter has been described herein in connection with variousembodiments and corresponding drawings, where applicable, it is to beunderstood that other similar embodiments can be used or modificationsand additions can be made to the described embodiments for performingthe same, similar, alternative, or substitute function of the disclosedsubject matter without deviating therefrom. Therefore, the disclosedsubject matter should not be limited to any single embodiment describedherein, but rather should be construed in breadth and scope inaccordance with the appended claims below.

What is claimed is:
 1. A system comprising, a processing unit that executes executable instructions to facilitate performance of operations, comprising: determining a temperature of an optical device; determining, based on the temperature of the optical device and a feedback model, a tuning input to stabilize an optical signal; and performing, based on the tuning input, feedback tuning, wherein the feedback tuning comprises thermal tuning and electrical tuning.
 2. The system of claim 1, wherein performing the thermal tuning comprises performing the thermal tuning with a pulse-width modulation circuit, a digital-to-analog conversion circuit, or a gain-tunable amplifier.
 3. The system of claim 1, wherein performing the electrical tuning comprises performing the electrical tuning with a digital-to-analog conversion circuit or a gain-tunable amplifier.
 4. The system of claim 1, wherein determining the electrical tuning comprising tuning a voltage or a current of the optical device.
 5. The system of claim 1, wherein the feedback model is based on historical tuning information associated with the optical device.
 6. The system of claim 1, wherein determining the tuning input comprises using machine learning to dynamically determine thermal effects and the feedback model, and wherein the feedback model is associated with the optical device.
 7. The system of claim 1, wherein determining the temperature of the optical device is based on a temperature sensitive integrated circuit.
 8. The system of claim 1, wherein the operations further comprise: determining predictions of a device and a surrounding environment of the device based on the temperature, a power level of a heater, or historical tuning information.
 9. The system of claim 8, wherein the predictions are further based on the feedback model, wherein the feedback model comprises a thermal model and an electrical model of the optical device, and wherein the optical device comprises a microresonator or a Mach-Zehnder Interferometer, and wherein the microresonator comprises a microring resonator or a microdisk resonator.
 10. The system of claim 1, wherein the system is: communicatively coupled to a photonic network, computationally coupled to a photonic processing system, or coupled to a hybrid photonic and electronic system.
 11. A method, comprising: determining, by a system operatively coupled to at least one processing unit, feedback from a group of photonic devices; determining, by the system and based on the feedback of the group of photonic devices, models associated with the group of photonic devices; and performing, by the system and based on the models, indirect feedback tuning, wherein the indirect feedback tuning comprises thermal tuning and electrical tuning.
 12. The method of claim 11, wherein the group of photonic devices comprises a photonic modulator, a photonic switch, or a photonic filter, and wherein determining the feedback does not utilize a port of a photonic device of the group of photonic devices.
 13. The method of claim 11, further comprising quantitively storing, by the system, empirical or test curves of predicted future feedback tuning or current feedback tuning.
 14. The method of claim 11, wherein the system is communicatively coupled to: a photonic network, a photonic processing system, or a hybrid photonic and electronic system.
 15. The method of claim 11, wherein the models comprise a model of the system.
 16. The method of claim 11, wherein the models comprise a model of a photonic device of the group of photonic devices.
 17. The method of claim 11, wherein the models comprise a model of an environment surrounding a photonic device of the group of photonic devices.
 18. A machine-readable storage medium, comprising executable instructions that, when executed by one or more processing units, facilitate performance of operations, comprising: determining indirect feedback tuning to perform on a photonic device, comprising: indirectly determining an optical signal of the photonic device, determining, based on the optical signal of the photonic device and a feedback model, the indirect feedback tuning required to decrease a bit error rate of the photonic device; and causing a model-based controller to apply the indirect feedback tuning.
 19. The machine-readable storage medium of claim 18, wherein the indirect feedback tuning comprises thermal tuning and electrical tuning.
 20. The machine-readable storage medium of claim 18, wherein the indirect feedback tuning is determined using machine learning to repeatedly determine thermal effects and the feedback model, and wherein the feedback model is associated with the photonic device. 